Graph Technology

A graph database is a specialized platform designed to store and manipulate data based on relationships. Unlike traditional relational databases, which rely on tables and rows, graph databases use a graph structure composed of three fundamental components:

• Nodes: These represent entities (such as people, products, or locations) in your data.
• Edges: They connect nodes and signify relationships between them.
• Properties: They provide additional information about nodes and edges.

The magic lies in how these components interact:

Graph databases are more than just repositories for data; they are a dynamic representation of relationships. Our team excels in designing and implementing robust graph data models. Whether you’re migrating from a relational database or starting from scratch, we’ll create a schema that captures the intricate connections within your domain. Our services include:

• Schema Design: Crafting efficient and expressive graph schemas tailored to your use case.
• Data Migration: Seamlessly transitioning your data to a graph database.
• Performance Optimization: Ensuring lightning-fast queries and traversals.

Unlock the hidden gems in your data using graph algorithms and analytics. Our data scientists specialize in extracting meaningful patterns from interconnected data. Our offer:

• Identify Key Players: Whether you want to identify bottlenecks in your supply chain, popular individuals in a social network or important parts in your bill of materials, centrality algorithms provide the answer for all these challenges.
• Find hidden clusters: Social networks, customer segments or fraud rings - these are all examples of hidden clusters within your data. Community detection techniques reveal these groups, allowing you to tailor marketing strategies, detect anomalies, or enhance security.
• Identify patterns: In Recommendation Engines, Protein Interaction Networks or Botnet Detection, entities with similar relationships and properties need to be identified. Similarity algorithms enable efficient data exploration and decision-making across diverse domains.

Imagine an AI that not only answers questions but generates contextually rich responses. Our approach combines a knowledge graph with generative AI models. Here’s how it works:

• Grounded Responses: The AI understands your business-specific data by leveraging the knowledge graph. Responses are accurate, relevant, and explainable.
• Vector Search: Implicit answers meet explicit facts. We seamlessly blend semantic meaning with factual accuracy by iterating through the graph providing context to the answer.
• Data Governance: Control information flow, access, and governance within the graph.

Do you need support with a project? Feel free to request a non-binding initial consultation - we will get back to you as soon as possible!